Cancer is one of the most life-threatening diseases worldwide, and head and neck (H&N) cancer is a prevalent type with hundreds of thousands of new cases recorded each year. Clinicians use medical imaging modalities such as computed tomography and positron emission tomography to detect the presence of a tumor, and they combine that information with clinical data for patient prognosis. The process is mostly challenging and time-consuming. Machine learning and deep learning can automate these tasks to help clinicians with highly promising results. This work studies two approaches for H&N tumor segmentation: (i) exploration and comparison of vision transformer (ViT)-based and convolutional neural network-based models; and (ii) proposal of a novel 2D perspective to working with 3D data. Furthermore, this work proposes two new architectures for the prognosis task. An ensemble of several models predicts patient outcomes (which won the HECKTOR 2021 challenge prognosis task), and a ViT-based framework concurrently performs patient outcome prediction and tumor segmentation, which outperforms the ensemble model.

该研究提出使用机器学习和深度学习自动进行头颈部肿瘤分割、患者预后预测等医学任务，并比较了基于ViT和卷积神经网络的模型，同时提出了两个新的架构，其中ViT模型的预测与分割结果优于集成模型，该成果最终在HECKTOR 2021挑战赛预测任务中获得胜利。

利用人工智能进行头颈部癌症患者的诊断和预测